Artificial intelligence and mathematical theory of computation
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Some contributions to the metatheory of the situation calculus
Journal of the ACM (JACM)
Reasoning about noisy sensors and effectors in the situation calculus
Artificial Intelligence
Using temporal logics to express search control knowledge for planning
Artificial Intelligence
Non-determinism and uncertainty in the situation calculus
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
The Mathematica Book
Decision-Theoretic, High-Level Agent Programming in the Situation Calculus
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Probabilistic propositional planning: representations and complexity
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Multi-Agent Systems Specification and Certification: A Situation and State Calculus Approach
Annals of Mathematics and Artificial Intelligence
Reasoning about actions with sensing under qualitative and probabilistic uncertainty
ACM Transactions on Computational Logic (TOCL)
Stochastic filtering in a probabilistic action model
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Proceedings of The Fourth International C* Conference on Computer Science and Software Engineering
Annotated Probabilistic Temporal Logic: Approximate Fixpoint Implementation
ACM Transactions on Computational Logic (TOCL)
Reasoning about continuous uncertainty in the situation calculus
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In this article we propose a Probabilistic Situation Calculus logical language to represent and reason with knowledge about dynamic worlds in which actions have uncertain effects. Uncertain effects are modeled by dividing an action into two subparts: a deterministic (agent produced) input and a probabilistic reaction (produced by nature). We assume that the probabilities of the reactions have known distributions.Our logical language is an extension to Situation Calculae in the style proposed by Raymond Reiter. There are three aspects to this work. First, we extend the language in order to accommodate the necessary distinctions (e.g., the separation of actions into inputs and reactions). Second, we develop the notion of Randomly Reactive Automata in order to specify the semantics of our Probabilistic Situation Calculus. Finally, we develop a reasoning system in MATHEMATICA capable of performing temporal projection in the Probabilistic Situation Calculus.